A cGAN network trained with the objective function in Eq. (REF ) is referred to as a Vanilla GAN. The least squares generative adversarial network (LSGAN) was proposed to overcome issues with stability during training of the Vanilla GAN [1]}. Its objective functions in a conditional setting are
\(\begin{split}\min _{{D}}V_{LSGAN}({D}) = & \frac{1}{2}\mathbb {E}_{x,{\widehat{z}_{y}}}[({D}(x,{\widehat{z}_{y}})-b)^2] + \\& \frac{1}{2}\mathbb {E}_{x}[( {D}(x,{G}(x))-a)^2]\\\min _{{G}}V_{LSGAN}({G}) = & \frac{1}{2}\mathbb {E}_{x}[( {D}(x,{G}(x))-c)^2],\end{split}\) 
